用大模型模拟跨语言数学辅导，验证母语反馈更有效
Simulating LLM-to-LLM Tutoring for Multilingual Math Feedback
- 让强模型当老师、弱模型当学生，模拟多语言教学互动
- 低资源语言中，母语提示使学习效果提升显著
- 适合开发公平普惠的多语言AI教育工具
大型语言模型（LLMs）已展现生成英语形成性反馈与指导提示的能力，日益应用于AI辅助教育。然而，其在不同语言间，尤其是基于数学推理的任务上提供有效教学支持的能力仍缺乏研究。本文首次大规模模拟多语言师生互动，由更强模型担任教师生成提示，较弱模型模拟学生。我们在11种语言类型多样、4个先进LLM及多种提示策略下，共开展352组实验，评估语言特异性反馈是否带来可测量的学习提升。研究考察了学生语言、教师反馈语言、模型选择与语言资源水平对表现的共同影响。结果表明，多语言提示能显著改善学习成果，尤其在低资源语言中，若反馈与学生母语一致，效果更佳。这些发现为开发高效且包容的多语言LLM教育工具提供了实践参考。
原文摘要 · Abstract (English)
Large language models (LLMs) have demonstrated the ability to generate formative feedback and instructional hints in English, making them increasingly relevant for AI-assisted education. However, their ability to provide effective instructional support across different languages, especially for mathematically grounded reasoning tasks, remains largely unexamined. In this work, we present the first large-scale simulation of multilingual tutor-student interactions using LLMs. A stronger model plays the role of the tutor, generating feedback in the form of hints, while a weaker model simulates the student. We explore 352 experimental settings across 11 typologically diverse languages, four state-of-the-art LLMs, and multiple prompting strategies to assess whether language-specific feedback leads to measurable learning gains. Our study examines how student input language, teacher feedback language, model choice, and language resource level jointly influence performance. Results show that multilingual hints can significantly improve learning outcomes, particularly in low-resource languages when feedback is aligned with the student's native language. These findings offer practical insights for developing multilingual, LLM-based educational tools that are both effective and inclusive.
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